The DCR method approximates the Laplacian pseudoinverse via regularized MLE, but its claimed solution reconstruction is a supervised fit to the CVXPY reference, making the numerical validation circular.
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Difference-of-Convex Regularization for Graph Learning by Differentiable Programming
The DCR method approximates the Laplacian pseudoinverse via regularized MLE, but its claimed solution reconstruction is a supervised fit to the CVXPY reference, making the numerical validation circular.